Dynamic feature-aware power management

ABSTRACT

This disclosure relates to reducing battery usage in handheld communication devices due to operation of variable managed features, such as a location determination feature and other features that consume variable amounts of time and power. The techniques of this disclosure include dynamically managing a power budget allocated to one or more variable managed features of the handheld communication device based on power consumption over time by the variable managed features. More specifically, the techniques include, for each variable managed feature, recalculating a frequency for performing power events based on an amount of remaining power after one or more power events. The techniques also include reallocating the power budget to the one or more variable managed features based on an amount of remaining power after one or more power events of the variable managed features, a pre-determined period of time, or reaching a threshold level of power consumption jitter.

This application claims the benefit of U.S. Provisional Application No.61/489,957, filed May 25, 2011, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The disclosure relates to power management in a handheld communicationdevice.

BACKGROUND

Handheld communication devices, such as mobile telephones, mobileemergency communication devices, mobile global positioning system (GPS)devices, portable computers with wireless communication cards, personaldigital assistants (PDAs), portable media players, or other flash memorydevices with wireless communication capabilities, are typically poweredby limited battery resources. Improved battery life and battery lifeconservation are, therefore, of paramount concern when designinghandheld communication devices. The concern for battery life is offset,however, by demands for increased features and applications on handheldcommunication devices. Typically, performance frequency and/or durationof features of the handheld communication device are related to powerconsumption. Limitations on power budgets allocated to the features ofthe device may have an effect on the accuracy and/or quality ofperformance of these features.

SUMMARY

In general, this disclosure relates to techniques for reducing batteryusage in handheld communication devices due to operation of variablemanaged features, such as a global positioning system (GPS) basedlocation determination feature and other features that consume avariable amount of time and power. The variable managed features of thehandheld communication device allow for a tradeoff between featureperformance and power usage. The techniques of this disclosure includedynamically managing a power budget allocated to one or more variablemanaged features of the handheld communication device based on powerconsumption over time by the variable managed features. Morespecifically, the techniques include, for each variable managed feature,recalculating a frequency for performing power events based on an amountof remaining power for the variable managed feature after one or morepower events. In addition, the techniques may include reallocating thepower budget to the one or more variable managed features based on anamount of remaining power after one or more power events of the variablemanaged features, a pre-determined period of time, or reaching athreshold level of power consumption jitter.

As an example, after each GPS based location determination, i.e., “fix,”a handheld communication device may recalculate when to perform asubsequent fix based on an amount of power remaining in the budgetallocated to the GPS-based location determination feature. The frequencywith which the handheld communication device performs each fix may bevaried because the amount of time, and therefore power, necessary toreturn each fix may vary based on satellite coverage or otherenvironmental influences. In this way, the techniques may enable thehandheld communication device to balance the frequency of a GPS fix withthe battery life of the device.

In one example, the disclosure is directed to a method of managing powerconsumption in a battery-powered device comprising determining an amountof power allocated to a variable managed feature supported by thedevice, performing power events of the variable managed feature, whereinperforming each of the power events consumes a variable amount of power,and recalculating a frequency with which to perform the power events ofthe variable managed feature based on an amount of remaining power forthe variable managed feature after one or more of the power events.

In another example, the disclosure is directed to a battery-powereddevice comprising means for determining an amount of power allocated toa variable managed feature supported by the device, means for performingpower events of the variable managed feature, wherein performing each ofthe power events consumes a variable amount of power, and means forrecalculating a frequency with which to perform the power events of thevariable managed feature based on an amount of remaining power for thevariable managed feature after one or more of the power events.

In another example, the disclosure is directed to a computer-readablemedium in a battery-powered device containing instructions. Theinstructions cause a programmable processor to determine an amount ofpower allocated to a variable managed feature supported by the device,perform power events of the variable managed feature, wherein performingeach of the power events consumes a variable amount of power, andrecalculate a frequency with which to perform the power events of thevariable managed feature based on an amount of remaining power for thevariable managed feature after one or more of the power events.

In another example, the disclosure is directed to a battery-powereddevice comprising a processor to determine an amount of power allocatedto a variable managed feature supported by the device, perform powerevents of the variable managed feature, wherein performing each of thepower events consumes a variable amount of power, and recalculate afrequency with which to perform the power events of the variable managedfeature based on an amount of remaining power for the variable managedfeature after one or more of the power events.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example handheld communicationdevice that utilizes the techniques of this disclosure.

FIG. 2A illustrates example power budget categories corresponding to afully charged battery.

FIG. 2B illustrates example power budget categories corresponding to apartially charged battery.

FIGS. 3A and 3B illustrate example power budget classifications forvariable managed budgets of a battery.

FIG. 4A illustrates an example fix distribution when no power budgetmanagement algorithm is utilized.

FIG. 4B illustrates an example fix distribution when a dynamic,feature-aware power budget management algorithm is utilized.

FIG. 5 is a flow chart of an example method of dynamic power budgetmanagement in accordance with this disclosure.

DETAILED DESCRIPTION

In general, this disclosure relates to managing battery usage in ahandheld communication device due to operation of features that usevariable amounts of time and power. These features may be defined asvariable managed features. Some examples of the variable managedfeatures may be global positioning system (GPS) based locationdetermination, other sensor-based features, and data transfer over acommunication link. The variable managed features allow for a tradeoffbetween feature performance and power usage.

In the example of battery-powered devices, it is generally important toconserve power and prolong the battery life as long as possible. Theperformance of features is often related to power consumption, andoptimizing the performance of some features may be subject tolimitations on the power budget, especially in smaller devices thatrequire smaller batteries. There is often a correlation between featureperformance and battery life. The techniques of this disclosure providean algorithm that optimizes the feature performance subject tolimitations on the power budget.

As an example, mobile emergency communication devices may be designedwith a small form factor, and require a small battery, in order to beworn on a user's clothing or wrist. At the same time, mobile emergencycommunication devices may require maintaining enough power to perform anemergency call, which may include two-way communication or at leastcommunication of the user's location. Mobile emergency communicationdevices, therefore, allocate a comparatively small power budget tocontinually perform a GPS-based location determination, i.e., “fix,” toensure the user's location is accurately obtained in case of anemergency. The continual performance of the location determination mayconsume a substantial amount of power, especially in areas with poorsatellite coverage. Reducing the frequency of performing the GPS-basedlocation determination, however, may negate the purpose of the mobileemergency communication device to provide accurate location informationfor the user.

The techniques of this disclosure may include dynamically managing apower budget allocated to one or more variable managed features of thehandheld communication device based on power consumption over time bythe variable managed features. More specifically, the techniquesinclude, for each variable managed feature, recalculating a frequencyfor performing power events based on an amount of remaining power forthe variable managed feature after one or more power events. Thetechniques may also include reallocating the power budget to the one ormore variable managed features based on an amount of remaining powerafter one or more power events of the variable managed features, apre-determined period of time, or reaching a threshold level of powerconsumption jitter.

In this disclosure, the example of a location determination feature isused to discuss the techniques of the disclosure. It should beunderstood that these techniques are likewise applicable to othervariable managed features that have associated power requirements andallow for a tradeoff between feature performance and power consumption.The location determination feature, e.g., one that may utilize GPS orother sensors, may be defined as a variable managed feature, because itallows for tradeoff between performance and power usage, and utilizes avariable amount of power each time it makes a location determination.For example, the less frequently a variable managed feature is used, theless power consumed by the operation of that feature, and the shortertime the variable managed feature operates in performing a power event,the less power consumed.

FIG. 1 is a block diagram illustrating an example handheld communicationdevice 100 that utilizes the techniques of this disclosure. Device 100may include processor 102, memory 104, sensor 106, variable managedfeature 108, battery 110, and transceiver 112. Variable managed feature108 may include one or more features whose performance may be managed bytechniques of this disclosure to reduce its power consumption. In oneexample, variable managed feature 108 may be a sensor-based feature, forexample, such as a GPS-based location determination feature. It shouldbe understood that device 100 may include other components, which maydepend on the type of device or other functionalities associated withthe device. The components shown in FIG. 1 are merely illustrative. Inone example, device 100 may be a communication device, such as a mobileemergency communication device, and may include transceiver 112 andother components associated with the communication functionality. Device100 may utilize transceiver 112 to communicate with an emergencyresponse system (e.g., 911) or an emergency contact number (e.g., arelative or caretaker of the person carrying device 100) by placing acall or sending location information. Transceiver 112 may transmit andreceive communications via a network, e.g., a cellular network.

Processor 102 may be operable to execute one or more algorithms thatinclude, for example, a dynamic feature-aware power budget managementalgorithm that optimizes power consumption associated with variablemanaged features. In one example, a power budget management (PBM) module114 may oversee the operation and execution of the dynamic power budgetmanagement algorithm. Processor 102 may also process data collected bysensor 106 and used by variable managed feature 108. Additionally,processor 102 may make determinations regarding power budgets associatedwith battery 110 and power usage from the operation of variable managedfeature 108, according to the techniques described in this disclosure.

Memory 104 may include one or more computer-readable storage media.Memory 104 may comprise one or more storage devices, capable oflong-term and short-term storage of information. Short-term storage ofmemory 104 may also be described as a volatile memory. Examples ofvolatile memories include random access memories (RAM), dynamic randomaccess memories (DRAM), static random access memories (SRAM), and otherforms of volatile memories known in the art. Long-term storage of memory104 may also be described as non-volatile memory. Examples of suchnon-volatile storage elements may include magnetic hard discs, opticaldiscs, floppy discs, flash memories, or forms of electricallyprogrammable memories (EPROM) or electrically erasable and programmable(EEPROM) memories.

In one example, memory 104 may be used to store program instructions forexecution by processor 102 such as, for example, dynamic feature-awarepower budget management algorithms associated with feature 108. Memory104 may be also used by software or applications running on device 100(e.g., sensor 106 and feature 108) to temporarily store informationduring program execution or during operation.

Sensor 106 may be one or more sensors that can collect data based onsensed data. For example, sensor 106 may be GPS or another locationdetermination capability. Variable managed feature 108 may include oneor more features that utilize data collected by sensor 106 to performone or more operations associated with device 100. In one example,feature 108 may be a GPS-based location determination feature thatutilizes location information from a GPS-based sensor (e.g., sensor 106)in its operation.

Variable managed feature 108 may be an application executed by processor102 that utilizes input from sensor 106. In one example, overall powerconsumption by device 100 may increase each time variable managedfeature 108 is used, i.e., when input from sensor 106 is requested andprocessed. The power consumption of device 100, therefore, may beaffected according to the frequency of use of variable managed feature108 and/or the duration of use of variable managed feature 108. As notedabove, while the example of a GPS-based location determination featureswill be used throughout this disclosure, it should be understood thatthe techniques of this disclosure are applicable to other features,which may be classified as variable managed features and utilize sensorsor inputs that increase overall power consumption by device 100.Furthermore, techniques of this disclosure are applicable to other typesof sensors and resources, which allow for tradeoffs between power usageand resource duration and/or performance.

In one example, management of the use of sensor 106 (e.g., GPS) andother hardware associated with the operation of sensor 106 becomes animportant factor in power budget management of the power sourceassociated with device 100, e.g., battery 110. In some examples, runningsensor 106 may consume a relatively large amount of power available frombattery 110. One conventional method of power budget management may beperformed by programming the location determination to operate in eithera high-power primary mode or a low-power fallback or secondary mode withassociated timeouts to switch between the modes. The techniques of thisdisclosure provide dynamic feature-aware power budget management thatmay further improve power budget management of battery 110.

In the example where sensor 106 is a location determination sensor,fixes (e.g., events during which a location is determined) take variableamounts of time, and therefore, consume variable amounts of power. Theamount of time needed for a fix may depend on various factors such as,for example, the GPS coverage which may depend on satellite signalstrength and the number of satellites within view or range. For example,the time to fix can vary by more than an order of magnitude between verypoor and very good satellite coverage. In some cases, the GPS may timeout while attempting to fix if the satellite coverage is insufficient.In some example, device 100 may integrate other location determinationcapabilities with the primary method (e.g., GPS) such that a fix isalways returned.

In a GPS-based location determination feature, there is a tradeoffbetween the desire to determine location frequently and the desire tolimit battery consumption. For example, the more frequently a locationfix is attempted and the longer the amount of time needed to achieve thelocation fix, the more power is consumed from battery 110 by variablemanaged feature 108. In general, there is a correlation between howfrequently location is determined and battery life. Specifically, thereis a direct relationship between how often and how long the locationdetermination engine runs and the battery life. As noted above, othertypes of variable managed features may include other types of sensors orany functionalities, for which the associated power consumption may varybased on changes to operation. For example, data transfer over acommunication link may have a tradeoff between the amount of datacompression and therefore accuracy of the data and the amount of powerconsumed in transferring the data. Techniques of this disclosure providean algorithm that manages the tradeoff between the operation of variablemanaged feature 108 and the battery life of battery 110. The techniquesprovide dynamic feature-aware power budget management that is sensitiveto the duration of the location determination.

FIGS. 2A and 2B illustrate example power budget categories correspondingto a fully charged battery and a partially charged battery,respectively, for a handheld communication device. Battery capacity of adevice battery (e.g., battery 110 of FIG. 1) may be divided into fourpower budget categories: reserve, fixed, variable non-managed, andvariable managed.

The reserve budget R may be a fixed amount of the battery capacity thatmay be reserved for essential functions when the device is operatingbetween charges. For example, the reserve budget R may guarantee that avoice call or a text message may be performed in case of an emergencyduring a given inter-charge time. When the device comprises a mobileemergency communication device, for example, the reserve budget for suchessential functions may be larger than in a mobile telephone or otherhandheld devices. For example, a mobile emergency communication devicemay use the reserve budget to transmit GPS-based location information,perform a two-way voice call of at least 20 minutes, transmit multipletext messages, transmit photographs or video, and the like.

The fixed budget Ft may be a fixed amount of battery capacity used torun constantly-required activities for operation of the device, such asrunning a processor or other peripheral components of the device. Forexample, the fixed budget may comprise a fixed utilization rate ofbattery capacity, e.g., 1 mA per hour.

The variable capacity Vt is a variable amount of battery capacityavailable to run activities that have variable power utilization rates.Activities with variable power utilization rates may be divided into twocategories: activities that may be managed for power control purposes orvariable managed budget activities (VMt), and activities which may notbe managed for power control purposes or variable non-managed budgetactivities (VNt). For example, some user or system requests for thedevice to perform certain activities, such as turn on, update a display,or play messages, may need to occur when requested and are considerednon-managed activities. In the example of non-managed activities, theremay be little to no flexibility in the duration and/or the frequency ofpower events associated with the requested non-managed activity.

As another example, some user or system requests may have theflexibility to be managed in terms of their frequency (e.g., how oftenthey occur) and duration (e.g., the length of time associated with eachoccurrence), and therefore are considered managed activities. As anexample of managed activities, a GPS-based location determinationfeature may allow for power budget management based on frequency andduration of location fixes performed by the feature. As another example,other system functions such as data transfers over wirelesscommunication links may also provide some flexibility in terms offrequency, duration, and level of data compression.

FIGS. 2A and 2B illustrate the difference in power budget categories fora fully charged battery (FIG. 2A) and a partially-depleted battery (FIG.2B). The fixed and reserve budgets may not change, because these budgetsmay be set aside independently of the expected battery life and areassociated with fixed amounts regardless of the other functionalities ofthe device. The techniques of this disclosure do not affect the fixedand reserve budgets of battery power, and therefore, in the example ofFIGS. 2A and 2B, the actual portions of the fixed and reserve budgetsthat have been utilized are not shown.

As FIG. 2B illustrates, the variable power budgets get smaller overtime, and each of the variable managed and variable non-managed budgetsmay shrink to reach zero at the end of the expected battery life. Themost efficient usage of the variable power budgets is exemplified bymeeting the expected battery life without running out of variable powerbudget before the end of the expected battery life, or having remainingvariable power budget at the end of the expected battery life.Techniques of this disclosure may allow management of the variable powerbudgets such that the variable budgets reach zero at the end of theexpected battery life.

In one example, the battery life requirement, B, may be the length oftime (e.g., in seconds) between the battery being fully charged and thebattery requiring recharge. The remaining battery life, BRt=(B−t), wheret is the time in seconds since the last battery recharge was completed.In one example, the techniques of this disclosure may provide a methodto optimize usage associated with a variable managed feature, e.g., aGPS-based location determination feature, while meeting other powerbudget requirements and the overall battery life requirement, as will beexplained in more detail below.

As noted above, the variable budget may be divided into two portions,the variable managed budget, VMt, and the variable non-managed budget,VNt. VNt may be estimated because the actual power budget needed may notbe fully known in advance, and may depend on such things as, forexample, requests to change the display or turning the power on and/oroff. As the battery life requirement goes from fully charged to fullydepleted, the VNt estimate may be periodically updated, e.g., to aconstant percentage value of Vt. As VNt is updated, VMt may be alsocalculated, where VMt=Vt−VNt. In this manner, in cases where VNt may beinitially overestimated, overestimates to VNt may be reassigned to VMt,instead of remaining unused while VMt is gets depleted. Similarly, incases where VNt may be underestimated, some of the VMt budget may bereassigned to VNt.

FIGS. 3A and 3B illustrate example power budget classifications forvariable managed budgets (VMt) of a battery in a handheld communicationdevice (e.g., device 100 of FIG. 1). In one example, VMt may be dividedinto two portions, as shown in FIG. 3A: variable managed budget for aprimary feature (e.g., a location determination feature), VMLt, andvariable managed budget for other variable managed features, VMOt.Therefore, VMt=VMLt+VMOt. As FIG. 3B illustrates, VMOt may be furtherdivided according to multiple variable managed features, VMO1 t, VMO2 t. . . , and VMOnt, each representing a sub-budget. VMLt and VMOt maycompete for the same budget, VMt. Algorithms may be implemented toarbitrate the sharing of the VMt budget between VMLt and VMOt.

In one example, the arbitrating algorithm may be implemented to allocatebudgets initially to the different features. The arbitrating algorithmmay also reallocate the budgets when needed, e.g., periodically, aftereach activity by one of the features that may affect the budgets, orafter reaching a power consumption jitter threshold. In one example, thearbitrating algorithm may allocate the budgets according to a simpleconfiguration, e.g., VMLt receives 50% and each of VMO1 t and VMO2 tgets 25%. The percentages may correspond to the power budget, totalpower requests by the features, or total time of activity of thefeatures. In another example, the arbitrating algorithm may employanother method to allocate the budgets, e.g., a greedy algorithm.

One example of a greedy algorithm, which may be used with the techniquesof this disclosure, may allocate the power budget to a requestingfeature, instead of having separate budgets with fixed percentages foreach of the features. This may result in power starvation if onefeatures uses up the full allocation. As a result, precautionarymeasures may be implemented to prevent one feature from utilizing theentire budget. The algorithm may be modified to allow allocation of thepower budget to a requesting feature by dynamically shrinking theallocation to the requesting (or greedy) feature once that feature hasconsumed an amount of power that is above a certain amount of theoverall allocation when other features are contending for the same powerbudget. In this manner, the greedy feature may be controlled duringcontention for the power budget by multiple entities, and allowed toacquire more resources when there is less or no contention for the powerresources. It should be noted that this is one example of a greedyalgorithm or an algorithm that arbitrates power budget resources. Otheralgorithms may be utilized to allocate and reallocate power budgetsamong the features as appropriate for the associated device.

In one example, techniques of this disclosure may provide algorithms fordynamically balancing performance of variable managed features withindevice 100 against the battery life requirements of battery 110. Asnoted above, a location determination feature is used herein as anillustrative example, but the same principles and techniques may beapplied to other features that utilize variable managed budgets, e.g.,data transfer over a communication link. In some examples, thealgorithms may be applied to features which use relatively large amountsof power. The algorithm may provide dynamic feature-aware power budgetmanagement and efficiently spread the power budget over the expectedremaining battery life. The power budget management algorithm may beapplied in a similar manner to one or more variable managed features. Inan example, where the algorithm may be applied to multiple features, thealgorithm may prioritize the features. Where the algorithm is appliedusing prioritization techniques, after applying the algorithm, theprioritization may be reassessed among the different features. In oneexample, the power budgets may be divided into sub-budgets according tomultiple features and the algorithm may be applied to each of thesub-budgets and the associated features. In this manner, the algorithmmay allocate amounts of power to each of the variable managed features,where the amount of allocated power may be based on prioritizationassociated with the features, e.g., a more important feature may getmore power than features considered less important. The algorithm maythen manage operational frequency of each of the features based on thecurrently-allocated amount of power, thus determining the frequency ofpower events associated with each of the features. The algorithm mayalso reallocate amounts of power to each of the features based onprioritization and the amount of power remaining, which may be affectedby performing power events associated with the features.

FIG. 4A illustrates an example fix distribution for alocation-determination feature when no power budget management algorithmis utilized. In this example, the start times for location determinationmay be evenly distributed over the projected battery life. In theexample of a GPS-based location determination feature, a power event maycorrespond to a fix or location determination event where the GPS moduleobtains data regarding the location of the device. In the example of amobile emergency communication device, the location determination may beperformed continually and transmitted in an emergency in order to locatethe person wearing the device when the emergency occurs.

The location may be determined at certain intervals, and may last for anamount of time that depends on the satellite signal strength. The amountof power consumed during a power event or a fix, e.g., a locationdetermination event, may depend on the amount of time it takes to obtainlocation data. In determining when to take the next fix for the locationdetermination feature, non-linear or linear distributions may beutilized to determine fix times. In one example, using lineardistributions may more evenly distribute the power budget over time,allow for a more constant performance of the associated feature, and, asa result, provide a relatively even feature performance over time. Thealgorithms used to implement the techniques of this disclosure arediscussed using a linear distribution approach, though a non-lineardistribution approach may also be used.

In the example of a location determination feature, during locationdetermination each fix result may correspond to a power event. A featurepower event may be an event associated with a feature that affects thepower budget by consuming battery power. Each fix may consume adifferent amount of power due to factors like satellite coverage and fixduration, for example. In one example, the fix events may be distributedevenly over the projected battery life to guarantee compliance. In oneexample, worst case power consumption may be utilized for each fix todetermine the distribution. Worst case power consumption may be based onhistorical or experimental data associated with the locationdetermination functionality, e.g., the GPS engine. As a result, by thetime the projected battery life is complete, it is likely that somepower budget may remain unutilized.

The techniques of this disclosure may further improve featureperformance by accounting for power budget that may remain unutilized.The following discussion assumes a single power event type per feature(e.g., location determination for a location determination feature) forillustrative purposes. In other cases, however, multiple power eventsmay be performed for each variable managed feature. In those cases, thesame principles and techniques may be scaled for multiple events and/ormultiple features.

As discussed above, FIG. 4A illustrates that the duration for eachlocation determination varies depending on factors, e.g., strength ofsatellite signal, number of satellites in range, and other environmentalconditions. The distribution may be based on the worst case powerconsumption, e.g., the fixes that require the longest time to completeindicated by the longer dashes. In this way, the handheld communicationdevice may ensure that each location fix will be complete beforeinitiating a subsequent location fix. As noted above, because not allfixes require the same amount of time, e.g., some require a short amountof time as indicated by shorter dashes, some of the power budget mayremain unutilized because the shorter fixes utilize less of the powerbudget. Therefore, feature performance may be improved by applying thedynamic feature-aware power budget management algorithm to the remainingpower budget that takes into account frequency and duration of the powerevents.

In one example, the dynamic power budget management algorithm mayutilize a value representing the power budget per event (e.g., fix). Thepower budget per event may be based on several factors such as, forexample, worst case power consumption per power event, average casepower consumption per power event, and best case power consumption perpower event. The worst, average, and best case power consumption perpower event values may be derived from historical data (e.g., usingactual samples from the device in which the algorithm is implemented),may be estimated, or may be a filtered number (e.g., based on the lasthour of operation).

The dynamic power budget management algorithm may utilize one or more ofthe values associated with power consumption per power event todetermine the distribution frequency of the events, e.g., the differencebetween start times for location determinations. In accordance with thetechnique, the distribution frequency may be updated over time, and maytherefore adapt regardless of which of the values, i.e., worst, average,or best case, the algorithm uses. In one example, average powerconsumption per power event may be utilized to determine the initialevent distribution frequency. Using the average power consumption perpower event may be based on the desire to achieve a constant level offeature performance. Additionally, while the distribution frequencyadapts over time regardless of the value of power consumption per powerevent used (i.e., best, worst, or average), using the average case powerconsumption per power event may result in the least amount of frequencyjitter, and may therefore be a more desirable approach.

The dynamic power budget management algorithm may also utilize otherparameters. For example, one parameter may define when the fixdistribution frequency should be recalculated, e.g., based on a timer,based on occurrence of one or more power events, and the like. In oneexample, a single power event may be utilized to trigger recalculationof fix distribution frequency. In another example, multiple events maybe utilized to trigger recalculation of fix distribution frequency. Inanother example, the algorithm may utilize a hybrid approach in whichinitially multiple power events may trigger the recalculation, but, asthe power budget nears exhaustion or completion, fewer power eventsand/or a single power event may trigger the recalculation. In anotherexample, power consumption jitter may be determined for a sequence ofevents and compared to a threshold level of power consumption jitter,and based on the comparison the algorithm may be modified. For example,if the amount of power consumption jitter exceeds the threshold, i.e.,plotting the values associated with power consumption exhibit jitterybehavior, the algorithm may be modified to minimize the jitter. Forexample, for a given variable managed feature, jitter may be minimizedby recalculating the frequency of fixes less frequently, and/or usingthe average case approach for the initial performance frequency adaptedaccording to the algorithm. In one example, when the jitter exceeds thethreshold, reallocation of the power budget may be triggered, where thealgorithm may reallocate the remaining power supply among all thevariable managed features according to their prioritization, asdiscussed above.

FIG. 4B illustrates an example fix distribution for a locationdetermination feature when the dynamic feature-aware power budgetmanagement algorithm is utilized. As FIG. 4B shows, when the duration ofthe location determination is short, as illustrated by short dashes, thealgorithm increases the frequency of the location determination, i.e.,location determination occurs more frequently. Likewise, when theduration of the location determination is long, as illustrated by longdashes, the algorithm decreases the frequency of the locationdetermination, i.e., location determination occurs less frequently,because long location determinations consume more power than the shorterones. Using the dynamic power budget management algorithm, the devicemay perform more location determinations while staying within the powerbudget, resulting in an improvement to the feature performance.

The dynamic power budget management algorithm may be illustrated by thefollowing example. Consider a linear distribution of the remaining powerbudged based on the fix time T (seconds), e.g., the time required tocomplete a location determination event. The fix time T may correspondto a power utilization of P (mAh) for the fix. In this illustrativeexample, T may be set to a constant value of an average fix time,corresponding to average case power consumption per power event. Inother examples, the algorithm may utilize the time associated with theworst or best case power consumption per power event values.Additionally, P may be a constant corresponding to the average casepower consumption per power event. In other examples, P may be set toone of the best or worst case power consumption per power event values.The algorithm may utilize any of the worst, best, or average values forP and T.

In this example, the algorithm may be set to recalculate the fixfrequency distribution after each power event. As noted above, thealgorithm may be modified to recalculate after a number of power events,or a hybrid approach where a number of power events to trigger arecalculation decreases as the end of the power budget is reached. Usingthese conditions and settings, the next fix time is:NFt=BRt/((VMLt/Pavg)+1),where NFt is the number of seconds from recalculation time, andrecalculation may be performed after a power event completes, e.g.,location data is acquired from a location determination event. BRt isthe battery life remaining in seconds, VMLt is the variable managedbudget for the location determination feature in mAh, and Pavg is theaverage power per event or fix in mAh.

In one illustrative example, after a location determination fix, assumeBRt is 360 seconds, VMLt is 36 mAh, and Pavg is 4 mAh. Then:NFt=360/((36/4)+1)=36.Therefore, the next location determination fix time will be 36 secondsfrom the time of performing recalculation, which may be performedfollowing the last location determination fix. In this example, 36seconds after recalculation, a fix is performed.

After every fix, the amount of time and power consumed by the fix may bedetermined to adjust the BRt and VLMt values, and recalculateaccordingly. The new value of BRt, BRt1, may be determined bysubtracting from the BRt value at the previous event (e.g., 360), BRt0,the sum of the time to the subsequent fix calculated for the previousevent, NFt0, and the fix time (T):BRt1=BRt0−(NFt0+T).Similarly, the new value of VMLt, VMLt1, may be determined bysubtracting from the VMLt values at the previous event (e.g., 36),VMLt0, the amount of power P used by the previous fix:VMLt1=VMLt0−PTherefore, the next fix time is:NFt1=BRt1/((VMLt1/Pavg)+1).

Assuming the next location determination fix consumes 4 seconds (T) and8 mAh (P). Then, after this location determination fix, the deviceupdates BRt to (360−(36+4))=320 seconds, and updates VMLt to (36−8)=28mAh. Then:NFt=320/((28/4)+1)=40 seconds.

As noted above, the example of the location determination feature ismerely illustrative, and the techniques of this disclosure may beapplied to other variable features in the device. In one example, themodification to the values of BRt and VMLt may depend on the powerevents associated with other variable managed features. In anotherexample, the power budgets may be apportioned for each variable managedfeature and the algorithm may be applied independently to each feature.

In addition to reassessing the power consumption and the frequency ofthe power events of each of the variable managed features individually,the algorithm may periodically assess the overall power budget for allthe variable managed features. The algorithm may periodically determine,based on the remaining power budget for all variable managed featuresand the prioritization, the amount of power allocated to each of thefeatures. The algorithm may perform the reallocation after at least onepower event of one of the variable managed features, after apre-determined period of time, or if power consumption jitter exceeds acertain threshold, for example. The reallocation of the amount of powerto each of the variable managed features may result in redistributingthe power budgets among the features, which may account for featuresconsuming power budgets at different rates.

While the techniques of this disclosure are described using the exampleof GPS-based location determinations and frequency of obtaining GPSdata, other types of sensors and techniques may be utilized. Forexample, the sensor may be an accelerometer, where the frequency ofobtaining acceleration measurements of the device may be varied usingthe techniques of this disclosure. In one example, the algorithm mayvary the format of data communicated over a communication channel suchthat, depending on the resources, more compressed data corresponding toa shorter transmission duration or less compressed data corresponding toa longer transmission duration may be utilized depending on a tradeoffbetween the length of transmission, the available power budgetremaining, and required transmission performance. In this example,different types and amounts of data compression may be utilized. In thecase of higher data compression, e.g., lossy compression, there may bemore errors and therefore lower quality, but the communication channelmay be used for a shorter time than in the case of higher quality andless compressed data. The dynamic feature-aware power budget managementalgorithm may determine whether to use higher or lower data compressionbased on an amount of remaining power budget.

FIG. 5 is a flow chart of an example method of dynamic power budgetmanagement in accordance with this disclosure. The illustrated examplemethod may be performed by device 100 (FIG. 1). In some examples, acomputer-readable storage medium (e.g., memory 104) may storeinstructions, modules, or algorithms (e.g., power budget management(PBM) module 114) that, when executed, causes one or more processors(e.g., processor 102) to perform one or more of the illustrated steps inthe flow chart.

The method of FIG. 5 includes allocating a variable managed power budgetto a plurality of variable managed features included in device 100(502). The amount of power allocated to the variable managed featuresmay depend on the power budget associated with a power source (e.g.,battery 110) of device 100. The variable managed features may includeone or more features associated with activities that have variable powerutilization rates and allow power budget management, e.g., a GPS-basedlocation determination feature or a data transfer feature. Each of thevariable managed features may have a priority associated with it. Theamount of power allocated to each of the variable managed features maybe based on the priority associated with the feature. The method furtherincludes determining the amount of the variable managed power budgetallocated to a first one of the variable managed features, e.g., thelocation determination feature (504).

Each of the variable managed features may perform power events where anactivity associated with the feature may occur, causing a certain amountof power to be consumed. For the first variable managed feature, e.g.,the location determination feature, one or more power events, e.g.,GPS-based location fixes, may be performed according to an initialfrequency (506). Each power event may consume an amount of power, whichmay depend on the duration of the event. The amount of power consumed byperforming power events may vary from one event to another according tovarious conditions, e.g., time needed to complete the power event,environmental condition, and the like.

The method also includes recalculating a frequency with which to performthe power events of the first variable managed feature (508). Thefrequency of performing the power events for a certain feature mayindicate the time between recalculating the frequency and the next timethe power event is performed. In one example, the frequency may berecalculated after every power event. In another example, the frequencymay be recalculated after performing N power events, where N isconstant. In yet another example, the frequency may be recalculatedafter performing N power events, where N may decrease as the amount ofpower allocated to the variable managed feature decreases and nearszero.

The recalculation of the frequency with which to perform the powerevents may be based on the amount of remaining power for the variablemanaged feature after performing the power events. The amount ofremaining power may be based on the difference between the amount ofpower available at the last recalculation and the amount of powerconsumed by power events since. After recalculating the frequency ofperforming the power events for the first variable managed feature, thepower events of the first variable managed feature may be performedaccording to the recalculated frequency (510). The frequency ofperforming the power events for the variable managed feature may berecalculated (508), as noted above, after every power event, after acertain number of power events, or other criteria.

In one example, the inner cycle of performing power events,recalculating frequency of performing power events, and performing powerevents at the recalculated frequency may be applied to each of thevariable managed features. In addition, the remaining variable managedpower budget associated with the variable managed features may bedetermined and reallocated to the plurality of variable managed features(512). The reallocation of the power to the variable managed featuresmay be based on an amount of remaining variable managed power budgetafter at least one of a power event of one of the variable managedfeatures, a pre-determined period of time, and a threshold level ofpower consumption jitter. The reallocation of power to each of thevariable managed features may be based on priorities associated with thefeatures.

In one example, the amount of power allocated may be the same as theamount of remaining power. In another example, the amount of powerallocated may be determined based on an overall power budget associatedwith other features, where the power budget may be redistributed amongfeatures according to their power consumption. The newly allocationamount of power for the first variable managed feature may again bedetermined (504), and the power events and frequency recalculation maybe performed for the first variable managed feature as described above.

As discussed above, the techniques of this disclosure describe a dynamicpower budget management algorithm in a handheld communication device. Insome examples, the dynamic power budget management algorithm may operatepassively, where it may not have direct control over the behavior of thefeatures it manages. In passive operation, the dynamic power budgetmanagement algorithm may respond and adapt to the behavior of thevariable managed features in a passive manner. For example, in theGPS-based location determination feature, the quality of the locationmay be set to allow for up to 30 seconds to perform a fix, meaning, afix occurs in 30 seconds or less. The dynamic power budget managementalgorithm may use the actual fix time as input to determine the fixfrequency. In this manner, the operation of the algorithm is passive.

In other examples, the dynamic power budget management algorithm mayoperate in a more active manner. For example, using the same example asabove, the GPS-based location determination feature, the default qualityof the location may also be set to allow for up to 30 seconds to performa fix. The dynamic power budget management algorithm may determine,based on certain tradeoff conditions, to increase or decrease thisamount of time. For example, it may be more critical that the powersource (e.g., battery) lasts 10 hours with lower quality fix, than tolast 5 hours with higher quality fix. The algorithm may then lower thequality of location from 30 seconds to 15 seconds, for example, whichmeans a fix will occur in 15 seconds or less. In this example, loweringthe quality of location may result in some compromise to the accuracy ofthe fix, as there will less time to resolve the location, but may resultin a tradeoff of extending battery life. In this manner, the operationof the algorithm is active, as it directly controls the operation of thefeature.

Using the data transfer over a communication link example, a minimumamount of data compression may be set as default. In a passiveoperation, the dynamic power budget management algorithm may use thedefault amount of data compression as input, based on which theadjustment to data compression may be determined. In an activeoperation, the dynamic power budget management algorithm may alter thedefault amount of data compression, or the minimum amount of datacompression to a higher level of compression. In this way, the accuracyof the transferred data may be compromised more, but results in atradeoff that extends battery life longer.

In one example, the techniques of this disclosure may be utilized withone or more power events that affect the power budget of a variablemanaged feature. For example, for a feature that utilizes locationdetermination, a set of location determination methods may be consideredby the dynamic power budget management algorithm. For example, thelocation determination methods may be sensor-based, short range radiobased, cellular-based, GPS-based, and the like. In one example, thevariable managed feature may utilize a subset of the methods todetermine location. During its operation, the dynamic power budgetmanagement algorithm may select the method that utilizes the leastamount of power in a given situation or under given conditions. Forexample, in certain geographical regions utilizing a GPS-based method todetermine location may consume less battery power than a cellular-basedmethod. In this example, the dynamic power budget management algorithmmay select the GPS-based method for location determination as part ofits operation to further reduce power consumption.

The techniques described in this disclosure may be implemented, at leastin part, in hardware, software, firmware, or any combination thereof.For example, various aspects of the described techniques may beimplemented within one or more processors, including one or moremicroprocessors, digital signal processors (DSPs), application specificintegrated circuits (ASICs), field programmable gate arrays (FPGAs), orany other equivalent integrated or discrete logic circuitry, as well asany combinations of such components. The term “processor” or “processingcircuitry” may generally refer to any of the foregoing logic circuitry,alone or in combination with other logic circuitry, or any otherequivalent circuitry. A control unit including hardware may also performone or more of the techniques of this disclosure.

Such hardware, software, and firmware may be implemented within the samedevice or within separate devices to support the various techniquesdescribed in this disclosure. In addition, any of the described units,modules or components may be implemented together or separately asdiscrete but interoperable logic devices. Depiction of differentfeatures as modules or units is intended to highlight differentfunctional aspects and does not necessarily imply that such modules orunits must be realized by separate hardware, firmware, or softwarecomponents. Rather, functionality associated with one or more modules orunits may be performed by separate hardware, firmware, or softwarecomponents, or integrated within common or separate hardware, firmware,or software components.

The techniques described in this disclosure may also be embodied orencoded in a computer-readable medium, such as a computer-readablestorage medium, containing instructions. Instructions embedded orencoded in a computer-readable medium, including a computer-readablestorage medium, may cause one or more programmable processors, or otherprocessors, to implement one or more of the techniques described herein,such as when instructions included or encoded in the computer-readablemedium are executed by the one or more processors. Computer readablestorage media may include random access memory (RAM), read only memory(ROM), programmable read only memory (PROM), erasable programmable readonly memory (EPROM), electronically erasable programmable read onlymemory (EEPROM), flash memory, a hard disk, a compact disc ROM (CD-ROM),a floppy disk, a cassette, magnetic media, optical media, or othercomputer readable media. In some examples, an article of manufacture maycomprise one or more computer-readable storage media.

In some examples, a computer-readable storage medium may comprise anon-transitory medium. The term “non-transitory” may indicate that thestorage medium is not embodied in a carrier wave or a propagated signal.In certain examples, a non-transitory storage medium may store data thatcan, over time, change (e.g., in RAM or cache).

Various examples have been described. These and other examples arewithin the scope of the following claims.

The invention claimed is:
 1. A method of managing power consumption in abattery-powered device comprising: determining an amount of powerallocated to a variable managed feature supported by the device;performing one or more power events of the variable managed featureaccording to a distribution frequency that indicates when to perform thepower events, wherein performing each of the power events consumes avariable amount of time and a variable amount of power; in response toperforming the one or more power events, determining remaining batterylife of the device based on the variable amount of time consumed byperforming the one or more power events, and determining an amount ofremaining power for the variable managed feature based on the variableamount of power consumed by performing the one or more power events; andrecalculating the distribution frequency that indicates when to performsubsequent power events of the variable managed feature based on theremaining battery life of the device after performing the one or morepower events, the amount of remaining power for the variable managedfeature after performing the one or more power events, and average powerconsumption per power event.
 2. The method of claim 1, whereinrecalculating the distribution frequency comprises recalculating thedistribution frequency based on the remaining battery life of the deviceand the amount of remaining power for the variable managed feature aftera first one of the power events, the method further comprisingperforming a subsequent power event of the variable managed featureaccording to the recalculated distribution frequency.
 3. The method ofclaim 2, wherein the recalculated distribution frequency comprises anamount of time between recalculating the distribution frequency andperforming the subsequent power event of the variable managed feature.4. The method of claim 1, wherein determining the amount of remainingpower for the variable managed feature comprises determining adifference between the amount of power allocated to the variable managedfeature and the variable amount of power consumed by performing the oneor more power events of the variable managed feature.
 5. The method ofclaim 1, further comprising allocating an amount of power to each of aplurality of variable managed features from a variable managed powerbudget, wherein the variable managed feature is one of the plurality ofvariable managed features.
 6. The method of claim 5, further comprisingreallocating an amount of power to each of the plurality of variablemanaged features based on an amount of remaining variable managed powerbudget after at least one of a power event of one of the variablemanaged features, a pre-determined period of time, or a threshold levelof power consumption jitter.
 7. The method of claim 1, wherein thevariable managed feature comprises a global positioning system (GPS)based location determination feature.
 8. The method of claim 1, whereinthe variable managed feature comprises a data transfer feature.
 9. Anon-transitory computer-readable storage medium in a battery-powereddevice containing instructions, the instructions cause a programmableprocessor to: determine an amount of power allocated to a variablemanaged feature supported by the device; perform one or more powerevents of the variable managed feature according to a distributionfrequency that indicates when to perform the power events, whereinperforming each of the power events consumes a variable amount of timeand a variable amount of power; in response to performing the one ormore power events, determine a remaining battery life of the devicebased on the variable amount of time consumed by performing the one ormore power events, and determine an amount of remaining power for thevariable managed feature based on the variable amount of power consumedby performing the one or more power events; and recalculate thedistribution frequency that indicates when to perform subsequent powerevents of the variable managed feature based on the remaining batterylife of the device after performing the one or more power events, theamount of remaining power for the variable managed feature afterperforming the one or more power events, and average power consumptionper power event.
 10. The non-transitory computer-readable storage mediumof claim 9, wherein the instructions to recalculate the distributionfrequency comprise instructions that cause the processor to recalculatethe distribution frequency based on the remaining battery life of thedevice and the amount of remaining power for the variable managedfeature after a first one of the power events, the computer-readablemedium further comprising instructions that cause the processor toperform a subsequent power event of the variable managed featureaccording to the recalculated distribution frequency.
 11. Thenon-transitory computer-readable storage medium of claim 10, wherein therecalculated distribution frequency comprises an amount of time betweenrecalculating the distribution frequency and performing the subsequentpower event of the variable managed feature.
 12. The non-transitorycomputer-readable storage medium of claim 9, wherein the instructions todetermine the amount of remaining power for the variable managed featurecomprise instructions that cause the processor to determine a differencebetween the amount of power allocated to the variable managed featureand the variable amount of power consumed by performing the one or morepower events of the variable managed feature.
 13. The non-transitorycomputer-readable storage medium of claim 9, further comprisinginstructions that cause the processor to allocate an amount of power toeach of a plurality of variable managed features from a variable managedpower budget, wherein the variable managed feature is one of theplurality of variable managed features.
 14. The non-transitorycomputer-readable storage medium of claim 13, further comprisinginstructions that cause the processor to reallocate an amount of powerto each of the plurality of variable managed features based on an amountof remaining variable managed power budget after at least one of a powerevent of one of the variable managed features, a pre-determined periodof time, or a threshold level of power consumption jitter.
 15. Thenon-transitory computer-readable storage medium of claim 9, wherein thevariable managed feature comprises a global positioning system (GPS)based location determination feature.
 16. The non-transitorycomputer-readable storage medium of claim 9, wherein the variablemanaged feature comprises a data transfer feature.
 17. A battery-powereddevice comprising: means for determining an amount of power allocated toa variable managed feature supported by the device; means for performingone or more power events of the variable managed feature according to adistribution frequency that indicates when to perform the power events,wherein performing each of the power events consumes a variable amountof time and a variable amount of power; means for determining, inresponse to performing the one or more power events, a remaining batterylife of the device based on the variable amount of time consumed byperforming the one or more power events, and an amount of remainingpower for the variable managed feature based on the variable amount ofpower consumed by performing the one or more power events; and means forrecalculating the distribution frequency that indicates when to performsubsequent power events of the variable managed feature based on theremaining battery life of the device after performing the one or morepower events, the amount of remaining power for the variable managedfeature after performing the one or more power events, and average powerconsumption per power event.
 18. The device of claim 17, wherein themeans for recalculating the distribution frequency comprises means forrecalculating the distribution frequency based on the remaining batterylife of the device and the amount of remaining power for the variablemanaged feature after a first one of the power events, the devicefurther comprising means for performing a subsequent power event of thevariable managed feature according to the recalculated distributionfrequency.
 19. The device of claim 18, wherein the recalculateddistribution frequency comprises an amount of time between recalculatingthe distribution frequency and performing the subsequent power event ofthe variable managed feature.
 20. The device of claim 17, wherein themeans for determining the amount of remaining power for the variablemanaged feature comprises means for determining a difference between theamount of power allocated to the variable managed feature and thevariable amount of power consumed by performing the one or more powerevents of the variable managed feature.
 21. The device of claim 17,further comprising means for allocating an amount of power to each of aplurality of variable managed features from a variable managed powerbudget, wherein the variable managed feature is one of the plurality ofvariable managed features.
 22. The device of claim 21, furthercomprising means for reallocating an amount of power to each of theplurality of variable managed features based on an amount of remainingvariable managed power budget after at least one of a power event of oneof the variable managed features, a pre-determined period of time, or athreshold level of power consumption jitter.
 23. The device of claim 17,wherein the variable managed feature comprises a global positioningsystem (GPS) based location determination feature.
 24. The device ofclaim 17, wherein the variable managed feature comprises a data transferfeature.
 25. A battery-powered device comprising: a battery; and aprocessor to: determine an amount of power from the battery allocated toa variable managed feature supported by the device, perform one or morepower events of the variable managed feature according to a distributionfrequency that indicates when to perform the power events, whereinperforming each of the power events consumes a variable amount of timeand a variable amount of power, determine, in response to performing theone or more power events, a remaining battery life of the battery basedon the variable amount of time consumed by performing the one or morepower events, and an amount of remaining power for the variable managedfeature based on the variable amount of power consumed by performing theone or more power events, and recalculate the distribution frequencythat indicates how frequently to perform subsequent power events of thevariable managed feature based on the remaining battery life of thebattery after performing the one or more power events, the amount ofremaining power for the variable managed feature after performing theone or more power events, and average power consumption per power event.26. The device of claim 25, wherein the processor is configured torecalculate the distribution frequency based on the remaining batterylife of the battery and the amount of remaining power for the variablemanaged feature after a first one of the power events, and the processoris further configured to perform a subsequent power event of thevariable managed feature according to the recalculated distributionfrequency.
 27. The device of claim 26, wherein the recalculateddistribution frequency comprises an amount of time between recalculatingthe distribution frequency and performing the subsequent power event ofthe variable managed feature.
 28. The device of claim 25, wherein, todetermine the amount of remaining power for the variable managedfeature, the processor determines a difference between the amount ofpower allocated to the variable managed feature and the variable amountof power consumed by performing the one or more power events of thevariable managed feature.
 29. The device of claim 25, wherein theprocessor allocates an amount of power to each of a plurality ofvariable managed features from a variable managed power budget, whereinthe variable managed feature is one of the plurality of variable managedfeatures.
 30. The device of claim 29, wherein the processor reallocatesan amount of power to each of the plurality of variable managed featuresbased on an amount of remaining variable managed power budget after atleast one of a power event of one of the variable managed features, apre-determined period of time, or a threshold level of power consumptionjitter.
 31. The device of claim 25, wherein the variable managed featurecomprises a global positioning system (GPS) based location determinationfeature.
 32. The device of claim 25, wherein the variable managedfeature comprises a data transfer feature.